Method and apparatus for estimating a pose

ABSTRACT

The invention relates to a real time-capable analysis of a sequence of electronic images for estimating the pose of a movable object captured by means of the images. The invention further relates to implementing the invention in software and, in this connection, to a computer-readable medium that stores commands, the execution of which causes the method according to the invention to be carried out. The invention proceeds from a skeleton model, which is described by a small number of nodes in 3D space and permits a good data compression of the image information when the co-ordinates of the nodes describe at any time the position of predetermined parts of the moving object. The skeleton model simultaneously represents previous knowledge of the object, by defining e.g. node pairs and optionally also node triplets in the skeleton model that describe cohesive object parts or optionally object surfaces, which are contained in the measured 2½-D image information, i.e. are visible to the camera. The skeleton model is to be fitted quickly and accurately into the image information. The fitting is effected between two images of an image sequence by the continuous displacement of the nodes and the continuous updating of the skeleton model.

FIELD OF THE INVENTION

The invention relates to a method and an apparatus for real time-capableanalysis of a sequence of electronic images for estimating the positionsand orientations of a movable object captured in the image sequence,i.e. estimating the pose of the movable object. The invention furtheralso relates to implementing the invention in software and in thiscontext to a computer-readable medium that stores commands the executionof which causes the method according to the invention to be carried out.

TECHNICAL BACKGROUND

Estimating human poses by a computer is the basis of agesture-controlled human-machine interaction. Body or hand gestures arecaptured by cameras, the captured digital images are processed in thecomputer and interpreted as commands that are eventually carried out bythe computer or by equipment controlled by it. The human user no longerrequires separate input equipment if he masters the commanding gestures.

Among particularly interesting areas of application of gesture controlare on the one hand the field of medical surgery where the operatingphysician would like to have direct control of auxiliary equipment (e.g.imaging devices such as ultrasound or MRT), but cannot touch any controldevices with his hands in order to safeguard sterility, and on the otherhand the field of public information terminals or ticket machines thatat present are still equipped with the rather unhygienic touch pads. Afurther field of application that has already been opened upcommercially is the computer game sector.

The purpose of a gesture-control method is to give the optical image ofa person a machine-interpretable meaning. This requires an apparatusthat images the person such that it can be evaluated electronically,compresses this image in terms of its information content and finallytranslates the compressed image of the person into amachine-interpretable output. The output of the apparatus can consist ofcontrol commands for downstream apparatuses to be controlled. However,it is also possible that it comprises only the compressed imageinformation that is fed to a downstream unit for interpreting thisinformation.

An example for compressed image information is for example thecontinuous output of the position coordinates of the right hand of theperson in a 3D coordinate system. In the process it is often sufficientto output only coordinates of a single point for the hand position, e.g.if the entire body of the person is imaged. If the motion of the personis imaged by an image sequence, the apparatus mentioned for exampleprovides the 3D coordinates of predetermined body parts that change overtime—during the motion. The coordinates can serve as variable inputsinto a program that e.g. accordingly controls a cursor position on ascreen.

During image segmentation, all recorded image data (measurement values)that cannot be assigned to the imaged person are removed, that is inparticular image elements that concern the background. Such imageelements have to be excluded from further evaluation.

Image segmentation using two-dimensional data is difficult above all ifthe user is imaged in front of a complex background—for example furtherpersons move in the background—or if he makes gestures where he movesextremities towards the camera such that they conceal part of his torso.Since gesture control is to take place in real time and pose estimationusually is to be possible at a video frame rate of 25 Hz or above, it isnecessary that image segmentation can take place within a fewmilliseconds. For this purpose, depth sensor cameras can be used thatcannot only measure, as conventional cameras, a brightness image, butalso the distance of the camera from the object.

A known depth sensor camera is called time-of-flight camera (TOF). Itemits infrared light whose intensity is modulated sinusoidally. Thephase displacement between the emitted light and the light reflected bythe object is measured in each pixel. From this phase displacement, thepropagation time (“time of flight”) of the light and thus the distanceof the camera from the object point can be calculated. A TOF cameraprovides a depth map that is in registry with a brightness image (in TOFnomenclature often called amplitude image).

A further method for simultaneously obtaining image and distancemeasurement values is based on structured light that is irradiated ontothe object to be measured and reflected by it. A camera detects thereflected light—usually at a different angle than the angle ofarrival—and registers the change of the structure of a projected patterndue to the position or extent of the reflected object surface. Forexample it is possible to calculate from the curvature of a reflectedline captured by the camera that was originally projected onto theobject as a straight line, a doming of the reflected surface, that is adistance variable relative to the projector and/or camera. In a similarway, a spatially divergent beam bundle is suitable that projects pointsin a three-dimensional scene, by detecting the point reflections anddetermining the distances between these. On a face located closer to theprojector, the point distances are less than on a face in the imagebackground. This is used for measuring the distances of faces or faceareas from the projector.

According to this, a depth sensor camera is an apparatus that alsoprovides distance information for each imaged object point in additionto a two-dimensional brightness image, so that in addition the positionof all imaged object points along a depth axis—that usually coincideswith the optical axis of the camera—is measured. The electronic imagehaving distance information recorded using a depth sensor camera is alsotermed a two and a half dimensional image (2½ D) of the scene. Theapparatuses mentioned above are only examples how 2½ D images can beproduced and do not necessarily represent a final list.

Among others, it can be gathered from the printed publication WO2010/130245 A1 how image segmentation of 2½ D images can take placecorrectly. Image segmentation orders the brightness values detected bythe camera pixels according to the distance values measuredsimultaneously and registered by the pixels. Only brightness values ofthe foreground remain in the further evaluation, it being assumed thatfor the purpose of improved visibility, the person to be observed isclosest to the camera. The brightness values of the foreground thusresult from imaging the body surface of the person. By means of thecamera projection parameters known per se, the imaged object points canthen each be assigned a set of 3D coordinates. A list of 3D coordinatesis then obtained that comprises all the points of the person that aredirectly visible for the camera. Inside this “cloud” of points in the 3Dspace there is the actual person, and inside the 3D point cloud thereare also the relevant coordinates of the predetermined body parts thatare desired to be determined for the purpose of gesture control.

The second part step of information compression can thus be seen indetermining from the 3D point cloud, determined by image segmentationand representing the person, a reduced set of point coordinates thatdescribes as best as possible an entire pose of the person and issuitable for machine interpretation. This step is also called poseestimation. One aim of pose estimation is here the robustness of thereduced data set, i.e. small changes of the human pose shall also leadonly to small changes in the data sets describing the pose. Inparticular the coordinates describing the human body parts shall, as faras possible, move on temporally continuous trajectories so that anunambiguous correlation of the coordinates with these body parts isgiven at any time.

A known and generally accepted approach is the definition of a skeletonmodel of the person that is to be fitted as fast as possible into the 3Dpoint cloud.

WO 2010/130245 A1 discloses a method for real time-capable poseestimation from sequences of 2½ D images, where a skeleton model isproposed that is explained as a topology of nodes and edges. The edgesthat can be described as pairs of nodes code a neighborhood structurebetween the nodes. The nodes are fitted into the previously determinedpoint cloud by applying a learning rule for training a self-organizingmap (“SOM”).

In the exemplary embodiment of WO 2010/130245 A1, the upper part of thehuman body is modelled using a topology from 44 nodes and 61 edges. The3D point cloud representing the person comprises approximately 6500 datapoints (depicted in the real 3D space in which the person observedexhibits a defined size independently from his distance from thecamera), of which approximately 10% are used for training an SOM. Allnodes of the topology can be directly regarded as an SOM, whilespecifying the edges can be regarded as a special requirement orlimitation for the learning rule.

The topology is trained separately for each frame of a video sequence,the training result of a frame at the same time serving to initializethe training of the following frame of the sequence. Duringinitialization of the first frame of a sequence the size of the topologyis preferably matched to the size of the person in front of the cameraby a one-off scaling, and its centre of gravity is displaced into thecentre of gravity of the 3D point cloud. If the size of the topology hasonce been selected correctly, it does not require further adaptingduring the on-going method, since the method functionsscale-invariantly. Training the frames takes place by applying apattern-by-pattern learning rule having the following steps:

a. randomly selecting a data point X of the 3D point cloud;b. determining that node of the topology that exhibits the minimumdistance from X;c. determining all neighbouring nodes of the node determined under b.according to the edge specification of the topology;d. displacing the nodes determined under b. and c. in the direction of X(see in this respect the equations (2) and (3) of WO 2010/130245 A1),e. the displacement vectors being multiplied by learning rates thatexhibit precisely half the size for the nodes determined under c. as forthe nodes determined under b. (see in this respect WO 2010/130245 A1, p.13, paragraph 4);f repeating the steps a. to e. for a predetermined number of learningsteps while gradually reducing the learning rates.

It is convenient to specify a maximum number of learning steps for eachframe for carrying out the pose estimation—i.e. in this case fitting theskeleton model into the 3D point cloud and reading out all relevantnodes positions—during a predetermined time interval. In this way, imagesequences can also be analysed at the video frame rate or even faster.

Although the algorithm of WO 2010/130245 A1 fulfils well the object ofreal-time pose estimation, it still does exhibit a few weaknesses thatare partly mentioned in the printed publication itself. In particularwhen analysing scenes where the person brings his arms together orcrossed them in front of the body, the learning rule can lead tomisinterpretations—that can be corrected during the course of furtheriterations—if individual nodes are pulled away far from their actualneighbours in the topology. This effect is countered in WO 2010/130245A1 with an anchoring point in the model torso and a secondary conditionof the learning rule that inhibits nodes displacements away from theanchoring point beyond a predetermined threshold.

The teaching of WO 2010/130245 A1 further also shows difficulties withthe precise position determination of human joints, shoulders, and hips,that can in each case be represented by several different nodes. Theskeleton model outlined in WO 2010/130245 A1 exhibits relatively manynodes, whose number cannot be readily reduced to 20 or less withoutaccepting considerable errors in the pose estimation. Systems that areavailable on the market for gesture control by means of depth sensorcameras already operate using skeleton models having 15-20 nodes ratherdesigned according to the human anatomy. By reducing the node count, ahigher processing speed of the camera images can also be obtained.

Anatomically motivated skeleton models are additionally suited forfalling back on stored movement patterns (templates) for detecting fastand complex movements (e.g. swinging a golf club). In these cases, thegesture-control software looks for the most likely match of the detectedpose change to a previously stored movement sequence and uses this knowntemplate for the actual control. This technology is already used incomputer games, but it is resource intensive. Last but not least,producing the stored movement data already gives rise to considerablecosts.

Gesture control by means of SOM training on the other hand completelydispenses with templates and is rather solely based on the realtime-capable detectability of the movement continuity. Due to learningrules that can be implemented efficiently, it has the potential toreliably detect even fast human movements and at the same time maintainsthe universal applicability so that possibly complex matching of thesoftware to the measurement task is omitted.

Abstract of the Invention

It is therefore the object of the invention to compress digital imageinformation of the camera image of an object, in particular for poseestimation, so that determination of the pose of the object, inparticular a human pose, can be carried out in real time with lesscomputational effort and thus faster and/or more precisely.

The object is achieved by the subject matter of the independent patentclaims. Advantageous embodiments of the invention are the subject matterof the dependent patent claims.

An aspect of the invention is thus information compression that usuallytakes place in two part steps: image segmentation and pose estimation.In this context, the invention in particular relates to improving poseestimation. According to the invention, pose estimation by means of SOMtraining is proposed that is able to work with a skeleton model that ismodelled according to the anatomy of the object observed and exhibits areduced node count, it being possible to reliably and consistentlyassign each model node to a predetermined body part. Here the anatomy ofthe object whose pose is to be detected is modelled as a skeleton model.

A skeleton model that is described only by a small number of points(“nodes” below) in the 3D space represents a good informationcompression of the image information if the coordinates of the nodes atall times describe the position of predetermined parts of the movingobject. At the same time, the skeleton model represents prior knowledgeon the object, in that e.g. node pairs and optionally also node tripletsare defined in the skeleton model that describe contiguous objects partsor optionally object faces that are contained in the measured 2½ D imageinformation, i.e. are visible for the camera. The skeleton model is tobe fitted fast and precisely into the image information that correspondsto the object. The fitting is effected between two images of an imagesequence by continuously displacing the nodes and updating the skeletonmodel in step with the image sequence. In the case of the contiguousobject parts or possible object faces already mentioned, it is assumedthat they move as a whole. According to the invention a node pair oroptionally a node triplet is thus displaced simultaneously underspecific preconditions. It has to be emphasized here that thedisplacement rules described further below not necessarily preserve thedistances of the nodes of a node pair or optionally node triplet, butthat the displacement can also lead to an increase in the distances ofthe nodes of a node pair or optionally a node triplet.

An embodiment of the invention refers to a method for pose estimation ofa moving object (e.g. a person or a robot) by computer calculation ofdisplacements of 3D position coordinates of the nodes of a skeletonmodel, that is continuously fitted into a sequence of 3D point clouds.The node coordinates are present in table form in an electronic memoryand the 3D point clouds are determined from electronically recordedimages from a depth sensor camera that represent the moving person. Theskeleton model is a topology that exhibits as topology elements N₁nodes, N₂ edges, and N₃ triangles having N₁, N₂>0 and N₃≧0, and eachtopology element being described by nodes, node pairs, or node tripletsand being firmly assigned to a part of the object (e.g. a human bodypart or part of a robot). The method is carried out by an arithmeticunit and comprises the following steps:

-   a. randomly selecting a data point X of the 3D point cloud;-   b. calculating the crossing point P relative to X with reference to    each topology element and identifying whether P lies in each case    inside the topology element;-   c. calculating the distance from X to each topology element as the    norm of the differential vector X-P;-   d. determining that topology element that exhibits the minimum    distance from X among all topology elements whose crossing point P    lies inside the topology element;-   e. displacing the topology element determined in step d by    displacing all nodes establishing the topology element in the    direction of the vector X-P, the displacement vector for a node    being multiplied by a learning rate and by a weight that results    from the crossing point P relative to X with reference to the    topology element determined in step d, and repeating the steps a.    to e. for a predetermined number of learning steps while gradually    reducing the learning rate;-   g. updating the node coordinates in the table of the electronic    memory after K passes of the predetermined number of learning steps    with K≧1;-   h. providing at least the node coordinates updated in the table for    further processing.

In a further embodiment of the invention in step b the crossing point Pwith reference to a topology element is represented as a linearcombination of the node coordinate vectors establishing the topologyelement, and it is determined from the representation coefficientswhether P lies inside the topology element.

In a further embodiment of the invention in step e the weight iscalculated from the representation coefficients of P.

In a further embodiment of the invention the number of repetitions ofthe steps a to e is between 1,000 and 5,000, in particular between 2,000and 3,000.

Advantageously the learning rate can lie between the starting value 0.5and the end value 0.01.

A further embodiment of the invention proposes an apparatus for poseestimation of a moving object. This apparatus comprises a depth sensorcamera, an electronic memory, and a programmable arithmetic unit, thememory storing the electronic images of the depth sensor camera and thearithmetic unit being designed to determine from the electronic images3D point clouds representing the object, temporally in step with theimage recording by the camera. The memory further stores a list of 3Dcoordinates for the nodes of a skeleton model. The arithmetic unit isable to read out and change these coordinates for individual nodes,predetermined node pairs, and predetermined node triplets asrepresentation of the topology elements of the skeleton model. Thearithmetic unit is further designed, after determining the 3D pointcloud representing the object, to carry out the following steps:

-   a. randomly selecting a data point X of the 3D point cloud;-   b. calculating the crossing point P relative to X with the reference    to each topology element and determining whether P lies in each case    inside the topology element;-   c. calculating the distance from X to each topology element as the    norm of the differential vector X-P;-   d. determining that topology element that exhibits the minimum    distance from X from all topology elements where the crossing point    P lies within the topology element;-   e. displacing the topology element determined in step d by    displacing all nodes establishing the topology element in the    direction of the vector X−P, the displacement vector for a node    being multiplied by a learning rate and by a weight that results    from the crossing point P relative to X with reference to the    topology element determined in step d, and-   f. repeating the steps a. to e. for a predetermined number of    learning steps while gradually reducing the learning rate;-   g. updating the node coordinates in the table of the electronic    memory after K passes of the predetermined number of learning steps    with K≧1;-   h. providing at least the node coordinates updated in the table for    further processing.

A further embodiment of the invention further relates to acomputer-readable storage medium that is characterized in that it storescommands that can be executed by a microprocessor that cause the latterto carry out the method for pose estimation according to one of thedescribed embodiments of the invention.

DESCRIPTION OF THE FIGURES

The invention is described below in more detail using exemplaryembodiments with reference to the figures. Elements and details in thefigures that correspond to each other have been provided with the samereference characters. In the drawings

FIG. 1: shows sketches of usable skeleton models: a) according to WO2010/130245 A1, b) model from nodes and edges, c) model from nodes,edges and triangles;

FIG. 2: shows a) an illustration of the learning rule from WO2010/130245 A1, b) geometrical interpretation of the weights of thelearning rule, and c) illustration of the effect of the learning rulefor the case of an edge displacement;

FIG. 3: shows a) a geometric interpretation of the weights of thelearning rule and b) representation of the effect of the learning rulefor the case of a triangle displacement;

FIG. 4: shows exemplary images having 3D point clouds and skeletonmodels fitted into these point clouds according to the invention.

DETAILED DESCRIPTION OF THE INVENTION

A skeleton model having only a low node count serves to effectivelycompress the image information when the coordinates of the nodes at anytime describe the position of predetermined parts of the moving object.The skeleton model is defined using prior knowledge about the object.For example there is prior knowledge on which contiguous object partsand optionally object faces are visible for the camera. Predeterminedelements of the skeleton model, in particular node pairs or nodetriplets, can represent these object parts or object faces alreadymentioned and be fitted as a whole into object images. This fittingtakes place between two images of an image sequence by constantlydisplacing the nodes and updating the skeleton model in step with theimage sequence. The contiguous object parts or object faces basicallymove as a whole, and according to the invention a node pair oroptionally a node triplet is therefore displaced simultaneously undercertain preconditions. In the process, the displacement rules do notnecessarily preserve the distances of the nodes of a node pair oroptionally node triplet relative to each other. The displacement canrather also lead to an increase in the distances of the nodes of a nodepair or optionally a node triplet. The inventive relinquishment ofcomplying with preserving the distances simplifies and accelerates therequisite calculations of the displacement vectors and all the sameleads to a good pose estimation. The accuracy of fitting the skeletonmodel into the object images increases with the number of iterations(learning steps).

The invention is described below substantially in the style of thedisclosure of WO 2010/130245 A1. It has to be remarked as a matter ofprinciple that the pose estimation described in WO 2010/130245 A1 can beregarded as the starting point and that at least one embodiment of theinvention can be regarded as a further development of the method forpose estimation known from this publication.

In this context, it is assumed in the following description of theembodiments of the invention that recording 2½ D-images (individually oras a sequence) and extracting a 3D point cloud representing the personcan be carried out as described in WO 2010/130245 A1. The invention inparticular assumes that images and point clouds for estimating a movedpose can be provided at a frequency of over 25 Hz.

According to the invention, an anatomically motivated skeleton model isnow used instead of the topology from WO 2010/130245 A1 that is seen asa comparison in FIG. 1 a). The model from FIG. 1 b) is particularlysuited that unambiguously associates each node with a distinctive pointof human anatomy (e.g. head, shoulder, elbow, hand, hip, pelvis, knee,foot). FIG. 1 c) represents a variant of the model from FIG. 1 b), wherethe torso is represented by triangles (in each case defined by threenodes that form the corners).

By identifying the nodes with human body parts, also the edges of themodels are given an anatomic interpretation. Thus for example in FIG. 1b) the edge that connects the nodes 1 (right hand) and 2 (right elbow)necessarily represents the right lower arm. Edges of the topologytherefore represent more than just a neighborhood relation of the nodes.Applying the learning rule for the SOM from WO 2010/130245 A1 can nolonger achieve good fitting of the model into the 3D point cloud forsuch a skeleton model that is markedly reduced in terms of its nodecount, so that a method for pose estimation is described below that isadapted to the inventive use of an anatomically motivated skeletonmodel.

In FIG. 2 a), two nodes W₁ and W₂ can be seen on the left side that areconnected by an edge. Point X designates a randomly selected point fromthe 3D point cloud into which the topology is to be fitted. According tothe teachings of WO 2010/130245 A1, at first the node of the model (inthis case: W₁) closest to the point X is determined and displaced in thedirection towards X by ε(X−W₁). Here c is a real positive number. Thenext neighbouring node in the sense of the topology is the node W₂connected to W₁ by means of the edge. It, too, is displaced in thedirection towards X by

$\frac{ɛ}{2}{\left( {X - W_{2}} \right).}$

The displaced nodes are marked on the right side. This learning rulealways shortens the edge between the nodes.

If the skeleton model comprises many nodes as in WO 2010/130245 A1, thenthe shortening processes are largely compensated again during the courseof time as part of further iterations and node displacements. However,an unambiguous association of certain nodes with certain body parts isnot always possible continuously.

For using the anatomic topologies of FIGS. 1 b) or c), according to theinvention new learning rules are therefore formulated that always permitthe association of nodes, edges, and optionally triangles of thetopology with body parts of the person.

An anatomic topology—or also: anatomic skeleton model—in the sense ofthe invention consists at least of a first number N₁ of nodes that areassociated with human body points, and a second number N₂ of edges thatare explained as a selection, satisfying anatomical facts, of pairs ofthe previously mentioned nodes.

To clarify: In FIG. 1 b), the model comprises the nodes W_(j) with j=1,. . . , 17 and further 17 edges that are represented by a selection ofnode pairs. For example the pairs (W₁, W₂), (W₂, W₃) or (W₄, W₈) arepart of the edges of the model, whereas for example pairs (W₂, W₆) or(W₁₀, W₁₁) do not represent edges.

In an advantageous design of the invention, a third number N₃ oftriangles can be part of the anatomical model. A triangle is describedby a triplet of nodes, the nodes designating the edges of the triangle.

In FIG. 1 c), the model inter alia consists of the three trianglesdescribed by the node triplets (W₃, W₄, W₉), (W₉, W₄, W₁₂) and (W₄, W₅,W₁₂).

Inserting triangles into the anatomical model is advantageous inparticular for modelling objects (that for example correspond to bodyregions), that exhibit only little internal mobility and usually move inthe image such that the relative position of the nodes forming the edgesof the triangle is changed only little relative to each other. This forexample applies to the torso of a person in an entire image, but canalso for example in the case of close-ups of a hand concern the palm orthe back of the hand. The user of the pose estimation method ultimatelyalways has to decide for himself which object is to be observed or whichtopology seems suitable and/or particularly favourable for his specificpurposes.

According to an embodiment, the present invention provides SOM learningrules for anatomical topologies from N₁ nodes, N₂ edges, and N₃triangles for fitting into 3D point clouds, N₁, N₂>0 and N₃≧0. Nodes,edges, and triangles are summarized below under the term topologyelements.

The SOM is identified with the nodes of the topology. The learning rulesare directed to displace the node positions such that the topology isfitted into a 3D point cloud. The specification of edges and optionallytriangles on top of this means that some pairs and possible sometriplets of nodes in each case have to obey inter-linked learning rules.

According to an embodiment of the invention, the learning rules areformed according to the following concept. Starting from the existenceof an image sequence for which continuous pose estimation is to becarried out, the skeleton model that is a list of node positions(described in a 3D coordinate system), node pairs, and optionally nodetriplets, is in each case updated when a new image of the sequenceexists. As soon as the new image is recorded by the depth sensor cameraand has been converted by means of image segmentation and projectioninto a list of 3D coordinates (3D point cloud) for the points of thesurface of the object observed—e.g. the entire person, his torso, hishand etc.—, SOM training takes place in that an individual point of thepoint cloud is randomly selected and the topology element closest tothis point is displaced in the direction of the selected point. Thedisplacement takes place by vector addition in the 3D space, as a resultof which individual node positions in the list of node positionsmentioned above are changed or updated.

After this displacement, the next point of the 3D point cloud israndomly selected, and a topology element—most likely a differentone—closest to the point now selected is displaced towards this point.Point selection and displacement are repeated for a predetermined numberof steps, the general displacement distance being reduced with eachstep. The skeleton model is eventually updated for the new image after asufficiently high predetermined number of steps.

Preferably at least 1,000 and at most 5,000 displacements of topologyelements are carried out for fitting the skeleton model into a 3D pointcloud. Particularly preferably the number of displacements is between2,000 and 3,000. Several 1,000 displacements of node positions areachievable with today's computers within a few milliseconds.

According to this it is possible that the topology element closest to aselected point is no node. Edges or triangles are displaced bydisplacing all nodes that determine an edge or a triangle, in aninter-linked manner. The displacement can also simultaneously concernone, two or three nodes depending on the topology element to bedisplaced.

The determination of the closest topology element is explained in moredetail below, and the specific displacement rules for the topologyelements are named.

At first the crossing points of X in terms of all topology elements areat first determined for a point X of the 3D point cloud—that is to sayfor a coordinate point in the 3D space that, after imaging of the objectobserved using a depth sensor camera and subsequent image segmenting andprojection, represents a point of the body surface of the person and israndomly selected from all these points. The crossing point P of X withreference to a topology element is that point of the sub space, formedby the topology element, of the 3D space that is closest to point X. Inthe process, nodes form zero-dimensional sub spaces that only containthe node itself Edges form straight lines in the 3D space that runthrough the nodes defining the edge. Triangles form planes in the 3Dspace that contain the nodes defining the triangle.

The point closest to point X, of a sub space formed by a topologyelement is calculated using a distance measures. Distances in the 3Dspace can also be determined using any norms. Preferably the Euclideannorm (also L₂ norm or Pythagoras distance) is used, but other distancemeasures can also be used.

The crossing point P is situated in a node if the topology element is anode, and it coincides here with just this node.

The crossing point P is situated on a straight line in the 3D space, ifthe topology element is an edge.

P=W+αΔW  (1)

Here W designates—any—first node of the edge, ΔW the differential vectorbetween the second and the first node of the edge, and a a real number.To clarify: The edge is described by the node pair (W, W+ΔW).

The crossing point P is situated on a plane in the 3D space if thetopology element is a triangle. The point P can be represented as

P=W+σ ₁ ΔW ₁+σ₂ ΔW ₂  (2)

with W as—any—first node of the triangle, ΔW₁, ΔW₂ as differentialvectors between the second and/or the third node and the first node ofthe triangle, and σ₁, σ₂ as real numbers. To clarify: The triangle isdescribed by the node triplet (W,W+ΔW₁,W+ΔW₂).

The coefficients α, σ₁, σ₂ from equations (1) and (2) shall bedesignated below as “topology-conforming representation coefficients” ofa crossing point. Formally, these are the components of the vector Prelative to a non-standardized and optionally an oblique-angled base ofthe sub space, formed by the topology element for which P is determined,of the 3D space. It is also possible to say that the crossing point Pwith reference to a topology element is represented as a linearcombination of the node coordinate vectors determining the topologyelement.

It is then checked whether the crossing points of a point X withreference to the topology elements lie inside these topology elements.

By definition all crossing points with reference to nodes lie inside thenodes. Likewise by definition, the topology-conforming representationcoefficient of a crossing point with reference to a node is alwaysunity.

A crossing point with reference to an edge is inside the edge preciselyif it is located between the nodes that define the edge. This means thata crossing point lies inside the edge if and only if its representationcoefficient α is between 0 and 1.

A crossing point with reference to a triangle is inside the triangleprecisely if it is inside the triangular surface that is defined by thenodes. For a crossing point with reference to a triangle, this is thecase if and only if its representation coefficients σ₁, σ₂ and their sumσ₁+σ₂ are between 0 and 1.

The distance of a point X of the 3D point cloud from a topology elementcan be calculated as a norm of the differential vector D between X andthe crossing point P, i.e. in the following D:=X−P. Preferably theEuclidean norm is used. In this way, the distance

d=∥D∥ ₂ =∥X−P∥ ₂=√{square root over ((x ₁ −p ₁)²+(x ₂ −p ₂)²+(x ₃ −p₃)²)}{square root over ((x ₁ −p ₁)²+(x ₂ −p ₂)²+(x ₃ −p ₃)²)}{squareroot over ((x ₁ −p ₁)²+(x ₂ −p ₂)²+(x ₃ −p ₃)²)}  (3)

with x₁, x₂, x₃, p₁, p₂, p₃ as real components of the vectors X and Pwith reference to the conventional 3D coordinate system, in which boththe 3D point cloud and also the skeleton model are described, iscalculated for each of the topology elements.

The distance of the point X from a topology element is further only usedif the crossing point P with reference to this topology element isinside the topology element. If this is not the case, the crossing pointis discarded, and either no distance is calculated or the calculateddistance is subsequently ignored.

This selection ensures that during further processing only thosedistances are taken into account that actually serve the purpose offitting the skeleton-model. The point X could for example be preciselyin a plane in the 3D space that is formed by a triangle that representsthe torso of the person. The crossing point P then coincides with X, andthe distance d is zero. If, however, at the same time the point issituated in the vicinity of the position of the node that represents theright hand, and the right arm is held extended away from the body, thedisplacement rule is to be applied to the node and not to the triangleof the torso, even if the numeric distance of its plane from X issmaller than that of the node from X.

From all calculated distances—and those remaining in the evaluation—, bylooking for the minimum, that topology element is identified that isclosest to the point X, i.e. exhibits the smallest distance from X.

According to the invention, the identified topology element is nowdisplaced, the nodes that define the topology element being possiblydisplaced jointly.

If the topology element closest to the point X is a node, then thedisplacement takes place according to

as is already known from the prior art. In WO 2010/130245 A1—equation(4) there—also the learning rate

$\begin{matrix}{ɛ_{t} = {ɛ_{i}\left( \frac{ɛ_{f}}{ɛ_{i}} \right)}^{\frac{t}{\tau_{\max}}}} & (5)\end{matrix}$

that is a function of the iteration step is explained. Here ε_(i) andε_(f) are predetermined starting and end values of the learning rate,and t_(max) is the predetermined maximum number of learning steps. Therunning index t counts the learning steps (iterations) up to t max. Foreach newly selected point X of the 3D point cloud, the numerator t isincreased by one until it reaches t_(max). Preferred stipulations forthe learning rate are ε_(i)=0.5 and ε_(f)=0.01.

If the topology element closest to point X is an edge, then thedisplacement takes place according to

In FIG. 2 b), the edge (W, W+ΔW), a point X of the 3D point cloud, andits associated crossing point P with reference to the edge and thedifferential vector D=X−P are drawn. The topology-conformingrepresentation coefficient α is to be regarded as that fraction of theedge length ∥ΔW∥₂ from which one has to walk in the direction ΔW,starting from the node W, to reach the point P. Obviously it is the casethat in FIG. 2 b) 0<α<1, and the crossing point is thus inside the edge.

FIG. 2 c) outlines the displacement of the two nodes. Both are moved inthe direction of the vector D=X−P, i.e. not—as in the prior art—directlytowards X. If point X is closer to node W than to node W+ΔW, this thenalso applies for the crossing point P, and consequently α<0.5. The nodeW is then displaced further than the node W+ΔW. In this way, the entireedge approaches the point X weighted according to the distance of Xand/or P from the nodes.

If the topology element closest to the point X is a triangle, then thedisplacement takes place according to

FIG. 3 illustrates the displacement rule for triangles.

FIG. 3 a) shows the starting triangle of the skeleton model and therandomly selected point X. The crossing point P associated with X is inthe triangle plane and can be represented as a linear combination of thenode coordinate vectors according to equation (2). For illustrationpurposes the representation coefficients σ₁, σ₂ are noted at the sidesof the triangles. They are to be interpreted similarly to thecoefficient α in FIG. 2 b) as fractions of the lengths of the trianglesides. The crossing point is inside the triangle, so that the distanceof X from the triangle is determined as the norm of the vector D=X−P andused during the further calculation. If this distance turns out to bethe smallest distance of X from all topology elements of the skeletonmodel, then the triangle is displaced.

The displaced triangle is outlined in FIG. 3 b). Here, too, allnodes—and together with these also all points on the sides of thetriangle and in the surface of the triangle—are displaced, accordinglyweighted with the original distance of the nodes from the points X andP. This weighting with the distance is important for the efficiency ofpose estimation since unnecessary errors are avoided thereby. Thisadvantage can be easily understood particularly when looking at FIG. 3b): If the point P is very close to one of the three nodes thatestablish the triangle, the triangle is displaced such that almost onlythis closest node is moved while the two others essentially maintaintheir position. There is thus a “smooth transition” between node, edge,and triangle displacement in the method described here.

From FIG. 2 c) it can be easily recognized that an edge (W, W+ΔW) is inno way shortened by applying the learning rules (6) and (7), but quitepossibly can be extended. The same holds for the sides a triangle (W,W+ΔW₁, W+ΔW₂) when applying the learning rules of equations (8) to (10).

So that no edge lengths and lengths of triangle sides that get out ofhand are obtained during the course of training, a “shrinking parameter”δ is introduced in a further embodiment of the invention. It can forexample be set as δ=0.05. At the same time, the learning rules aremodified such that during displacement, the nodes are slightly movedtowards each other.

Instead of rules (6) and (7) preferably

are used, and instead of the equations of rules (8) to (10)

are used.

The method described can be used for estimating the human pose bycalculating displacements of nodes of a skeleton model that is modelledaccording to human anatomy as in FIG. 1. However, it is also obviousthat the same method can likewise be applied to movements of an animalor of a moving robot. The invention is to comprise the pose estimationof all objects that consist of parts that are interconnected and canmove relative to each other and for which a skeleton model from nodes,edges, and optionally triangles can be conveniently defined.

The skeleton model is a table with 3D coordinates for the nodes of thetopology and a list having predetermined node pairs and optionally nodetriplets for establishing edges and triangles. The list of node pairsand node triplets cannot be changed, only the node coordinates canchange during the execution of the pose estimation. The table havingnode coordinates is available in an electronic memory and can be readout by an arithmetic unit. The arithmetic unit determines thedisplacements of the node positions relative to the continuous fittingof the skeleton model into the point clouds using a likewise storedimage from the depth sensor camera and a 3D point cloud determinedtherefrom.

Fitting the anatomical model into the point cloud—consequently trainingthe SOM—takes place according to an embodiment of the invention,summarized by:

-   a. randomly selecting a data point X of the 3D point cloud;-   b. calculating the crossing point P relative to X with the reference    to a topology element and determining its at least one    topology-conforming representation coefficient;-   c. discarding a crossing point if it does not lie inside the    topology element;-   d. calculating the distance from X to the topology element as a norm    of the differential vector X−P;-   e. repeating the steps b. to d. for all topology elements of the    anatomical model;-   f. determining that topology element that exhibits the minimum    distance from X;-   g. displacing the topology element determined under f. by displacing    all nodes establishing the topology element in the direction of the    vector X−P, the displacement vectors being multiplied by a learning    rate and by weights that result from the topology-conforming    representation coefficients of crossing point P relative to X with    reference to the topology element determined under e., and-   h. repeating the steps a. to g. for a predetermined number of    learning steps while gradually reducing the learning rate.

The precise manner how the weights mentioned in step g result from thetopology-conforming representation coefficients can be gathered fromequations (6) to (10) or as an alternative (11) to (15), the latter onescomprising an additional, predetermined parameter δ. By definition, theweights for the displacement of topology elements that are nodes areunity according to equation (4).

If the predetermined number of learning steps has been reached, thearithmetic unit has calculated displaced coordinates for all nodes ofthe skeleton model. The displaced coordinates, as a rule, will deviatefrom the originally tabulated node coordinates for all nodes, but inexceptional cases can also be identical to these for individual nodes.

The displaced node coordinates are written into the electronic memory bythe arithmetic unit, the original coordinate entries either i) beingoverwritten or ii) being designated as outdated and no longer used infurther learning steps. In this way, the node coordinates are updated inthe table of the electronic memory. Advantageously, it is possible tocarry out the step of updating the node coordinates in the table of theelectronic memory after only one pass of the predetermined number oflearning steps and then to use the subsequent image of the imagesequence from the depth sensor camera and the point cloud determinedtherefrom to start the next pass of the steps a to h.

Likewise it is also possible to cycle through the learning steps for oneand the same point cloud several times one after the other to calculatea plurality of displacements of node coordinates. This plurality ofdisplacements can then be averaged for example arithmetically, and thenode coordinates are updated in the table of the electronic memory onlytogether with the averaged displacement. This procedure is morecomplicated in terms of computation and thus slower but can alsoeffectively suppress any accidentally occurring unfavourabledisplacements (e.g. if the point cloud also comprises points with anoutsider position that are selected at random).

Broadly speaking, updating the node coordinates therefore takes placeafter K passes of the predetermined number of learning steps, K≧1.

An exemplary embodiment for the inventive pose estimation method withthe skeleton model from FIG. 1 b) is presented in FIG. 4 using exemplaryimages from video sequences. The video images of a depth sensor cameraconstantly provide 3D point clouds that represent a moving person, usingimage segmentation that is known per se. The anatomic skeleton model isfitted in real time using the learning rules described above, and theassociation of the model nodes with the different body parts of theperson remains correct throughout as can be clearly gathered from FIG.4.

The robust association node—body part that is now achieved permits amarkedly more stable command input by means of gesture control, forexample simply by tracking the movements of the right hand. A machinethat interprets the skeleton-node positions can ignore, as is necessary,the remaining model nodes or also classify them as additional inputs.Thus it is for example possible to carry out more complex inputs bysimultaneously and separately tracking and interpreting the movements ofthe right hand and of the left hand. Lifting the left hand to the levelof a head can for example be equivalent to pressing a certain key (e.g.shift key) on a keyboard or retrieve another set of commandinterpretations for the inputs of the right hand.

In addition to fast detection and provision of 2½ images by means of adepth sensor camera, the method described also requires an electronicmemory at least for temporarily storing the measurement data and forstoring position coordinates of the topology elements of the anatomicskeleton model and also an electronic arithmetic unit communicating withthe memory that carries out the calculations described and in particularprompts the continuous update of the position coordinates of thetopology elements in the memory. Furthermore, the same arithmetic unitcan either on its own interpret the respective current skeleton modelkeeping in step temporally and for example translate it into controlcommands for subordinate apparatuses, or possibly only output selectednode positions for further interpretation.

As was described at the beginning, the method described for poseestimation can be carried out by an apparatus, in particular anarithmetic unit. The arithmetic unit can be a commercially availableprogrammable microprocessor, but it is also possible to use FPGAs orASICs. A further embodiment of the invention relates to a storage mediumthat stores commands that can be carried out by an arithmetic unit andthat cause the arithmetic unit to carry out the calculations describedhere for the purpose of pose estimation.

A depth sensor camera that comprises an arithmetic unit for determiningthe distance coordinate can in particular also be engineered directly tocarry out the inventive method. Such a camera as a constructional unithaving at least one arithmetic unit designed according to the inventionis correspondingly suited to directly translate the image of a movingperson into 3D coordinates of his essential body parts. This iscomparable to a motion capture apparatus where, however, the markers onthe body of the person that until now were common, can be dispensedwith.

1. A method for pose estimation of a moving object by computercalculation of displacements of the 3D position coordinates of the nodesof a skeleton model, that is continuously fitted into a sequence of 3Dpoint clouds, the node coordinates being present in table form in anelectronic memory and the 3D point clouds being determined fromelectronically recorded images of a depth sensor camera that representthe moving object, the skeleton model being a topology that exhibits astopology elements N₁ nodes, N₂ edges, and N₃ triangles having N₁, N₂>0and N₃≧0, and each topology element being described by nodes, nodepairs, or node triplets and being firmly assigned to a part of themoving object, and that an arithmetic unit carries out the followingsteps: a. randomly selecting a data point X of the 3D point cloud; b.calculating the crossing point P relative to X with reference to eachtopology element and identifying whether P lies in each case inside thetopology element; c. calculating the distance from X to each topologyelement as the norm of the differential vector X−P; d. determining thattopology element that exhibits the minimum distance from X among alltopology elements whose crossing point P lies inside the topologyelement; e. displacing the topology element determined in step d bydisplacing all nodes establishing the topology element in the directionof the vector X−P, the displacement vector for a node being multipliedby a learning rate and by a weight that results from the crossing pointP relative to X with reference to the topology element determined instep d, and f. repeating the steps a to e for a predetermined number oflearning steps while gradually reducing the learning rate; g. updatingthe node coordinates in the table of the electronic memory after Kpasses of the predetermined number of learning steps with K≧1; h.providing at least the node coordinates updated in the table for furtherprocessing.
 2. The method according to claim 1, wherein in step b thecrossing point P with reference to a topology element is represented asa linear combination of the node coordinate vectors establishing thetopology element and it is determined from the representationcoefficients whether P lies inside the topology element.
 3. The methodaccording to claim 1, wherein the weight in step e is calculated fromthe representation coefficients of P.
 4. The method according to claim1, the number of repetitions of the steps a to e being between 1,000 and5,000, in particular between 2,000 and 3,000.
 5. The method according toclaim 1, the learning rate being between the starting value 0.5 and theend value 0.01.
 6. The method according to claim 1, further comprisinggenerating control commands for an apparatus to be controlled on thebasis of information from the table of the updated node coordinates andof controlling the apparatus to be controlled by means of the controlcommands.
 7. An apparatus for pose estimation of a moving object,comprising: a depth sensor camera configured to detect electronic imagesof the moving object, an electronic memory configured to store theelectronic images of the depth sensor camera, and an arithmetic unitthat is designed to determine a 3D point cloud representing the objectfrom the electronic images temporally in step with the image recordingby the camera, and wherein the memory further stores a list of 3Dcoordinates for the nodes of a skeleton model and the arithmetic unitbeing able to read out and change these coordinates for individualnodes, predetermined node pairs, and predetermined node triplets asrepresentation of the topology elements of the skeleton model, whereinthe arithmetic unit is further designed, after determining the 3D pointcloud representing the object, to carry out the following steps: a.randomly selecting a data point X of the 3D point cloud; b. calculatingthe crossing point P relative to X with reference to each topologyelement and identifying whether P lies in each case inside the topologyelement; c. calculating the distance from X to each topology element asthe norm of the differential vector X−P; d. determining that topologyelement that exhibits the minimum distance from X among all topologyelements where the crossing point P lies within the topology element; e.displacing the topology element determined in step d by displacing allnodes establishing the topology element in the direction of the vectorX−P, the displacement vector for a node being multiplied by a learningrate and by a weight that results from the crossing point P relative toX with reference to the topology element determined in step d, and f.repeating the steps a to e for a predetermined number of learning stepswhile gradually reducing the learning rate; g. updating the nodecoordinates in the table of the electronic memory after K passes of thepredetermined number of learning steps with K≧1; h. providing at leastthe node coordinates updated in the table for further processing.
 8. Anon-transitory computer-readable storage medium that stores commandsthat, once executed by an arithmetic unit, cause the arithmetic unit tocarry out 6 pose estimation of a moving object by computer calculationof displacements of the 3D position coordinates of the nodes of askeleton model, that is continuously fitted into a sequence of 3D pointclouds, the node coordinates being present in table form in anelectronic memory and the 3D point clouds being determined fromelectronically recorded images of a depth sensor camera that representthe moving object, the skeleton model being a topology that exhibits astopology elements N₁ nodes, N₂ edges, and N₃ triangles having N₁, N₂>0and N₃≧0, and each topology element being described by nodes, nodepairs, or node triplets and being firmly assigned to a part of themoving object, wherein the execution of said instructions cause thearithmetic unit to perform the following: a. randomly selecting a datapoint X of the 3D point cloud; b. calculating the crossing point Prelative to X with reference to each topology element and identifyingwhether P lies in each case inside the topology element; c. calculatingthe distance from X to each topology element as the norm of thedifferential vector X−P; d. determining that topology element thatexhibits the minimum distance from X among all topology elements whosecrossing point P lies inside the topology element; e. displacing thetopology element determined in step d by displacing all nodesestablishing the topology element in the direction of the vector X−P,the displacement vector for a node being multiplied by a learning rateand by a weight that results from the crossing point P relative to Xwith reference to the topology element determined in step d, and f.repeating the steps a to e for a predetermined number of learning stepswhile gradually reducing the learning rate; g. updating the nodecoordinates in the table of the electronic memory after K passes of thepredetermined number of learning steps with K≧1; h. providing at leastthe node coordinates updated in the table for further processing.